code: def _magic_fit(
      self,
      x=None,
      y=None,
      batch_size=None,
      epochs=1,
      # FIXME: there is more
      # verbose=1,
      # callbacks=None,
      # validation_split=0.,
      # validation_data=None,
      # shuffle=True,
      # class_weight=None,
      # sample_weight=None,
      # initial_epoch=0,
      # steps_per_epoch=None,
      # validation_steps=None,
      # validation_freq=1,
      # max_queue_size=10,
      # workers=1,
      # use_multiprocessing=False,
      *args,
      **kwargs,
  ):
      if hasattr(self, "_wandb_internal_model"):
          return self._fit(
              x=x,
              y=y,
              batch_size=batch_size,
              epochs=epochs,
              *args,
              **kwargs,
          )
      return _fit_wrapper(
          self,
          self._fit,
          x=x,
          y=y,
          batch_size=batch_size,
          epochs=epochs,
          *args,
          **kwargs,
      )
  
imports: import argparse
  import copy
  import json
  import os
  import re
  import sys
  import yaml
  import wandb
  from wandb import trigger
  from wandb.util import add_import_hook, get_optional_module
label_desc: Use image node_id 7 for calling a global function with example usage: _magic_fit(x, y, batch_size, epochs) and returns: _fit_wrapper, self